BFS and DFS are both vital for graph findings. BFS stands for Breadth-First Search, and DFS stands for Depth-First Search. Both of them have a distinction between the two.
Both of them even uses different data structure for the functioning, one using the Queue data structure, another being the Stack data structure.
- Breadth-first search (BFS) processes nodes in layers, visiting all neighbors before moving to the next layer.
- Depth-first search (DFS) explores the depth of a branch before backtracking and exploring other branches.
- BFS uses more memory than DFS but can find the shortest path, while DFS uses less memory but may traverse longer paths.
BFS vs DFS
The difference between BFS and DFS is that Breadth-First Search is a technique based on the vertex that helps in pointing out the shortest path in a graph. On the other hand, the DFS or Depth First Search is a technique that is based on edge. The BFS is a technique that depends on the queue data structure. On the other hand, the DFS depends on the Stack data structure.
BFS is a technique that is applied to figuring out the shortest route in a graph by using a Queue data structure. It is suitable for finding vertices within close areas of the source.
Unlike DFS, it cannot be better applied in decision-making trees found in puzzles or games.
DFS is a technique that is applied to finding out the shortest path in a graph using the Stack data structure. It is suitable for areas far from the sources within the solution.
Unlike BFS, it can be better applied to in-game decision-making or problem-solving.
|Parameters Of Comparison||BFS||DFS|
|Full form and definition||BFS stands for Breadth-First Search. It is a technique based on the vertex that is used for finding the shortest route in the graph.||DFS stands for Depth First Search. It is a technique based on edges for finding the shortest route in the graph.|
|Dependence on data structure||Breadth-First Search or the BFS figures out the shortest path in a graph with the help of Queue data structure.||Depth First Search or the DFS figures out the shortest path in a graph with the help of Stack data structure.|
|Uses||In an unweighted graph, it is used for finding the shortest path of a single source as it uses the least number of edges from a vertex source.||In DFS, for reaching a point of destination or vertex from any source, more edges should be traversed through.|
|Area of suitability||Its area of suitability for finding vertices ranges within close range of the source. It is not suitable for making decision trees present in games.||Its area of suitability ranges within solutions far from the source. It is more suitable for decision-making or problems in games or puzzles.|
|Mechanism||In this technique, a single vertex is chosen at a time during its visit and marked after which adjacent is visited and stocked in the queue.||The vertices visited are put into the stack and then in the absence of vertices, the vertices visited are popped.|
What is BFS?
With the help of BFS, the graph is traversed in a bread toward motion. To remember to fetch the next vertex, a queue is used in this technique.
This occurs when it faces a dead end in an iteration. It is not considered for decision trees as it vastly embraces all neighbours. It is comparatively slow than DFS.
The BFS algorithm BFS’s time complexity is O(V+E) during the time of the Adjacent list and O(V^2) during the time of the adjacency matrix. Here, E stands for edges, and V stands for vertices.
The BFS algorithm in a graph can be used in different fields.
BFS is highly used for coining each and every neighbour node in peer-to-peer connections. Search engine crawlers build the index with the help of this technique.
It helps find all associated links, ranging from source to new pages. It is required to find neighbouring places with the help of a GPS navigation facility.
The BFS algorithm is used while broadcasting some packets in networking. The algorithm of pathfinding encompasses BFS.
With the help of this technique, the highest flow in a network can be found in the Ford-Fulkerson algorithm.
What is DFS?
With the help of DFS, a graph is traversed in depthward motion. A stack is used in this technique to get reminded of getting the point next to the former one.
The search is done during the occurrence of any iteration. During the decision tree, further traverse should be made for augmentation of the decision.
In conclusion, victory is acknowledged. It is comparatively fast in speed than BFS. DFS’s time complexity is O(V+E) during the Adjacent list and O(V^2) during the adjacent matrix.
Here, E stands for edges, and V stands for vertices. DFS is vastly used in different fields.
When DFS is performed on the unweighted graph, minimum spanning trees are developed for every shortest path tree pair. It can be applied to identifying cycles in graphs.
If one back edge is found in BFS, then there is one cycle. The path among u and v or two vertices can be found with the help of this technique.
DFS algorithm is used for topological sorting. It can be used to determine the components strongly linked in a given graph.
Components are coined to be strongly connected when there is a path between each vertex.
Main Differences between BFS and DFS
- The full form of BFS is Breadth-First Search, and DFS is Depth First Search.
- BFS is a vertex-centric technique and DFS is an edge-centric technique.
- BFS utilizes Queue data structure, but DFS works with Stack Dara structure.
- BFS works to figure the shortest path of a single source since it uses the minimum count of edges from a vertex origin. On the other hand, the DFS utilizes more edges for traversing to reach a point of destination or Vertex.
- BFS is not suitable for getting applied in decision-making trees that are in puzzles or games. On the other hand, DFS is suitable for problem-solving in games.
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Sandeep Bhandari holds a Bachelor of Engineering in Computers from Thapar University (2006). He has 20 years of experience in the technology field. He has a keen interest in various technical fields, including database systems, computer networks, and programming. You can read more about him on his bio page.